{"doi":"10.1002/ecy.2759","title":"Analyzing community structure subject to incomplete sampling: hierarchical community model vs. canonical ordinations","abstract":"<jats:title>Abstract</jats:title><jats:p>Recently developing hierarchical community models (<jats:styled-content style=\"fixed-case\">HCM</jats:styled-content>s) accounting for incomplete sampling are promising approaches to understand community organization. However, pros and cons of incorporating incomplete sampling in the analysis and related design issues remain unknown. In this study, we compared<jats:styled-content style=\"fixed-case\">HCM</jats:styled-content>and canonical redundancy analysis (<jats:styled-content style=\"fixed-case\">RDA</jats:styled-content>) carried out with 10 different dissimilarity coefficients to evaluate how each approach restores true community abundance data sampled with imperfect detection. We conducted simulation experiments with varying numbers of sampling sites, visits, mean detectability and mean abundance. Performance of<jats:styled-content style=\"fixed-case\">HCM</jats:styled-content>was measured by estimates of “expected” (mean) abundance () and realized abundance (: direct estimate of site‐ and species‐specific abundance). We also compared<jats:styled-content style=\"fixed-case\">HCM</jats:styled-content>and different types of<jats:styled-content style=\"fixed-case\">RDA</jats:styled-content>(normal, partial, and weighted), all performed with the same ten different dissimilarity coefficients, with unequal number of visits to sampling sites. In addition, we applied the models to a virtual survey carried out on the Barro Colorado Island tree plot data for which we know true community abundance. Simulation experiments showed that yielded by<jats:styled-content style=\"fixed-case\">HCM</jats:styled-content>best restored the underlying abundance of constituent species among 12 abundance estimates by<jats:styled-content style=\"fixed-case\">HCM</jats:styled-content>and<jats:styled-content style=\"fixed-case\">RDA</jats:styled-content>regardless if the sampling was equal or unequal. Mean abundance predominantly affected the performance of<jats:styled-content style=\"fixed-case\">HCM</jats:styled-content>and<jats:styled-content style=\"fixed-case\">RDA</jats:styled-content>while yielded by<jats:styled-content style=\"fixed-case\">HCM</jats:styled-content>had comparable performance to percentage difference and Gower dissimilarity coefficients of<jats:styled-content style=\"fixed-case\">RDA</jats:styled-content>. Relative performance of<jats:styled-content style=\"fixed-case\">RDA</jats:styled-content>types depended on the combination of dissimilarity coefficients and the distribution of sampling effort. Best performance of followed by , percentage difference and Gower dissimilarity were also observed for the analysis of tree plot data, and graphical plots (triplots) based on rather than clearly separated the effects of two environmental covariates on the abundance of constituent species. Under our conditions of model evaluation and the method, we concluded that, in terms of assessing the environmental dependence of abundance,<jats:styled-content style=\"fixed-case\">HCM</jats:styled-content>s and<jats:styled-content style=\"fixed-case\">RDA</jats:styled-content>can have comparable performance if we can choose appropriate dissimilarity coefficients for<jats:styled-content style=\"fixed-case\">RDA</jats:styled-content>. However, since<jats:styled-content style=\"fixed-case\">HCM</jats:styled-content>s provide straightforward biological interpretations of parameter estimates and flexibility of the analysis,<jats:styled-content style=\"fixed-case\">HCM</jats:styled-content>s would be useful in many situations as well as conventional canonical ordinations.</jats:p>","journal":"Ecology","year":2019,"id":666255,"datarank":0.3596842909197557,"base_score":2.3978952727983707,"endowment":2.3978952727983707,"self_citation_contribution":0.3596842909197557,"citation_network_contribution":0.0,"self_endowment_contribution":0.3596842909197557,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":10,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1739868,"name":"F. Guillaume Blanchet","orcid":null,"position":1,"is_corresponding":false},{"id":1739869,"name":"Motoki Higa","orcid":null,"position":2,"is_corresponding":false},{"id":1739866,"name":"Yuichi Yamaura","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Analyzing community structure subject to incomplete sampling: hierarchical community model vs. canonical ordinations","abstract":"<jats:title>Abstract</jats:title><jats:p>Recently developing hierarchical community models (<jats:styled-content style=\"fixed-case\">HCM</jats:styled-content>s) accounting for incomplete sampling are promising approaches to understand community organization. However, pros and cons of incorporating incomplete sampling in the analysis and related design issues remain unknown. In this study, we compared<jats:styled-content style=\"fixed-case\">HCM</jats:styled-content>and canonical redundancy analysis (<jats:styled-content style=\"fixed-case\">RDA</jats:styled-content>) carried out with 10 different dissimilarity coefficients to evaluate how each approach restores true community abundance data sampled with imperfect detection. We conducted simulation experiments with varying numbers of sampling sites, visits, mean detectability and mean abundance. Performance of<jats:styled-content style=\"fixed-case\">HCM</jats:styled-content>was measured by estimates of “expected” (mean) abundance () and realized abundance (: direct estimate of site‐ and species‐specific abundance). We also compared<jats:styled-content style=\"fixed-case\">HCM</jats:styled-content>and different types of<jats:styled-content style=\"fixed-case\">RDA</jats:styled-content>(normal, partial, and weighted), all performed with the same ten different dissimilarity coefficients, with unequal number of visits to sampling sites. In addition, we applied the models to a virtual survey carried out on the Barro Colorado Island tree plot data for which we know true community abundance. Simulation experiments showed that yielded by<jats:styled-content style=\"fixed-case\">HCM</jats:styled-content>best restored the underlying abundance of constituent species among 12 abundance estimates by<jats:styled-content style=\"fixed-case\">HCM</jats:styled-content>and<jats:styled-content style=\"fixed-case\">RDA</jats:styled-content>regardless if the sampling was equal or unequal. Mean abundance predominantly affected the performance of<jats:styled-content style=\"fixed-case\">HCM</jats:styled-content>and<jats:styled-content style=\"fixed-case\">RDA</jats:styled-content>while yielded by<jats:styled-content style=\"fixed-case\">HCM</jats:styled-content>had comparable performance to percentage difference and Gower dissimilarity coefficients of<jats:styled-content style=\"fixed-case\">RDA</jats:styled-content>. Relative performance of<jats:styled-content style=\"fixed-case\">RDA</jats:styled-content>types depended on the combination of dissimilarity coefficients and the distribution of sampling effort. Best performance of followed by , percentage difference and Gower dissimilarity were also observed for the analysis of tree plot data, and graphical plots (triplots) based on rather than clearly separated the effects of two environmental covariates on the abundance of constituent species. Under our conditions of model evaluation and the method, we concluded that, in terms of assessing the environmental dependence of abundance,<jats:styled-content style=\"fixed-case\">HCM</jats:styled-content>s and<jats:styled-content style=\"fixed-case\">RDA</jats:styled-content>can have comparable performance if we can choose appropriate dissimilarity coefficients for<jats:styled-content style=\"fixed-case\">RDA</jats:styled-content>. However, since<jats:styled-content style=\"fixed-case\">HCM</jats:styled-content>s provide straightforward biological interpretations of parameter estimates and flexibility of the analysis,<jats:styled-content style=\"fixed-case\">HCM</jats:styled-content>s would be useful in many situations as well as conventional canonical ordinations.</jats:p>","is_dataset_classified":null,"base_score":2.3978952727983707,"endowment":2.3978952727983707,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"31131887","pmcid":null,"openalex_id":"https://openalex.org/W2947355813","authors":[],"funders":[],"total_grants":0,"fwci":0.7931,"citation_percentile":0.76441578,"influential_citations":0,"citation_trend":[{"year":2020,"count":1},{"year":2021,"count":4},{"year":2024,"count":4},{"year":2026,"count":1}],"oa_status":"closed","license":"http://onlinelibrary.wiley.com/termsAndConditions#vor","oa_locations":[{"url":"https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fecy.2759","host_type":"publisher"},{"url":"https://onlinelibrary.wiley.com/doi/pdf/10.1002/ecy.2759","host_type":"publisher"},{"url":"https://onlinelibrary.wiley.com/doi/full-xml/10.1002/ecy.2759","host_type":"publisher"},{"url":"https://esajournals.onlinelibrary.wiley.com/doi/pdf/10.1002/ecy.2759","host_type":"publisher"},{"url":"https://doi.org/10.1002/ecy.2759","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/31131887","host_type":"repository"}],"fields_of_study":["Species Distribution and Climate Change","Ecology and Vegetation Dynamics Studies","Fire effects on ecosystems","Colorado","Models, Biological"],"mesh_terms":["Colorado","Models, Biological"],"keywords":["Abundance (ecology)","Sampling (signal processing)","Statistics","Relative species abundance","Community structure","Mathematics","Ecology","Biology","Computer science","correlation matrix","Covariance Matrix","Sampling Design","Sampling Effort","Rv Coefficient","N-mixture Model","Redundancy Analysis (Rda)","Dissimilarity Coefficient","Hierarchical Community Model (Hcm)","Partial Rda","Triplot","Weighted Rda"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Life in Land"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-13T13:52:23.788818Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}